Continuous Profiling of Pavement Surface Friction Using a Mobile Robotic Platform

Addressing the critical need for agile and comprehensive road assessment, this study introduces an intelligent monitoring system for pavement skid resistance based on a Robot Operating System (ROS) mobile platform. The system is engineered to overcome the inefficiencies and discrete nature of traditional point‐based methods by enabling continuous, “line‐based” dynamic friction profiling. Built upon an Ackermann‐steering robot, the system integrates a high‐precision force‐sensing system and standard rubber sliders to dynamically calculate the friction coefficient from real‐time horizontal and vertical force data. Field experiments conducted under a low load (14.7 N) and across a velocity gradient from 0.5 to 1.5 m/s demonstrated the device's stability and revealed a pavement‐specific velocity dependence: on the smooth surface, the friction coefficient monotonically decreased with increasing velocity, aligning with classical theory, whereas on the asphalt pavement, it exhibited a complex and non‐monotonic behavior, featuring a distinct peak at 1.0 m/s that experimentally highlights the friction‐enhancement effect of dynamic vibrations at low speeds. Validation against conventional methods (BPN) confirmed a highly consistent overall trend. The primary contribution of this work lies in its ability to capture high‐resolution, non‐monotonic friction curves, providing indispensable physical insights for refining low‐speed friction models. This automated and efficient approach offers an innovative paradigm for intelligent road inspection.